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A multi-voxel-activity-based feature selection method for human cognitive states classification by functional magnetic resonance imaging data

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Abstract

Nowadays, various kinds of signals and data were collected to investigate human brain’s activities for disease detection. In particular, the functional magnetic resonance imaging (fMRI) provides a powerful tool for enquiring the brain functions. Learning the activity patterns that are related to the specific cognitive states from fMRI data is one of the most critical challenges for neuroscientists. The high dimensional property and noises make fMRI data become difficulty for mining and unfamiliar with conventional approaches. In this paper, we propose a new feature selection method for classifying human cognitive states from fMRI data. The fisher discriminant ratio (FDR) between classes and zero condition is used to measure the activity of voxels. We then choose the most active voxels from the most active regions of interest (ROIs) as the most informative features for Gaussian naïve bayes (GNB) classifier. The proposed method can be used to boost the whole system because it will exclude the non-task-related components and therefore, reduce the processing time and increase the accuracy. The StarPlus dataset and Visual object recognition dataset are used to investigate the performance of the proposed method. The experimental results show that our proposed method has better performance compared to other systems. The accuracy is \(\sim \)96.45 % for StarPlus dataset and 88.4 % for Visual Object Recognition dataset.

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References

  1. Mahoui, M., Lu, L., Gao, N., Li, N., Chen, J., Bukhres, O., Miled, Z.B.: A dynamic workflow approach for the integration of bioinformatics services. Cluster Comput. 8(4), 279–291 (2005)

    Article  Google Scholar 

  2. Chen, D., Lu, D., Tian, M., He, S., Wang, S., Tian, J., Cai, C., Li, X.: Towards energy-efficient parallel analysis of neural signals. Cluster Comput. 16(1), 39–53 (2013)

    Article  Google Scholar 

  3. Plaza, J., Pérez, R., Plaza, A., Martínez, P., Valencia, D.: Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms. Cluster Comput. 11(1), 17–32 (2008)

    Article  Google Scholar 

  4. S. Lee, Y. Baik, K. Nam, J. Ahn, Y. Lee, S. Oh, K. Kim, “Developing a cognitive evaluation method for serious game engineers”, Cluster Computing, 2013.

  5. Lindquist, M.A.: The statistical analysis of fMRI data. Stat. Sci. 28, 439–464 (2008)

    Article  MathSciNet  Google Scholar 

  6. Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10(9), 424–430 (2006)

    Article  Google Scholar 

  7. T.M. Mitchell, R. Hutchinson, R.S. Niculescu, F. Pereira, X. Wang and M. Just, “Classifying Instantaneous Cognitive States from fMRI data”, American Medical Informatics Association Symposium, 465–469 (2003)

  8. B.M. Bly, “When you have a General Linear Hammer, every fMRI time-series looks like independent identically distributed nails”, Concepts and Methods in NeuroImaging Workshop, 2001.

  9. Friston, K.J., Holmes, A.P., Worsley, K., Poline, J.B., Frith, C.D., Frackowiak, R.S.J.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189–210 (1995)

    Article  Google Scholar 

  10. P.A.d.F.R. Højen-Sørensen, L.K. Hansen and C.E. Rasmussen, “Bayesian modeling of fMRI time series”, Proc. Conf. Advances in Neural Information Processing Systems, NIPS, 754–760 (1999)

  11. Jung, T., Makeig, S., McKeown, M., Bell, A., Lee, T., Sejnowski, T.: Imaging brain dynamics using independent component analysis. Proc. IEEE 89, 1107–1122 (2001)

    Google Scholar 

  12. Jung, T., Makeig, S., McKeown, M., Bell, A., Kinderman, S., Sejnowski, T.: Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 6, 160–188 (1998)

    Article  Google Scholar 

  13. Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Astouchen, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001)

    Article  Google Scholar 

  14. Cox, D.D., Savoy, R.L.: Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 19, 261–270 (2003)

    Article  Google Scholar 

  15. M.T.T. Hoang, Y.G. Won and H.J. Yang, “Cognitive States Detection in fMRI Data Analysis using incremental PCA”, ICCSA. 335–341 (2007)

  16. F. Yong, D. Shen and C. Davatzikos, “Detecting Cognitive States from fMRI Images by Machine Learning and Multivariate Classification”, Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition, Workshop (2006)

  17. Etzel, J.A., Gazzola, V., Keysers, C.: An introduction to anatomical ROI-based fMRI classification analysis. Brain Res. 1282, 114–125 (2009)

    Article  Google Scholar 

  18. Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X., Just, M., Newman, S.: Learning to decode cognitive states from brain images. Mach. Learn. 57, 145–175 (2004)

    Article  MATH  Google Scholar 

  19. R.S. Bapi, V.Singh and K.P. Miyapuram, “Detection of Cognitive States from fMRI data using Machine Learning Techniques”, IJCAI. 587–592 (2007)

  20. N. Bernard, A. Vahdat, G. Hamarneh and R. Abugharbieh, “Generalized Sparse Classifiers for Decoding Cognitive States in fMRI”, Proceedings of the First international conference on Machine learning in medical imaging, 108–115 (2010)

  21. Rademacher, J., Galaburda, A.M., Kennedy, D.N., Filipek, P.A., Caviness, V.S.: Human celebral cortex: localization, parcellation, and morphometry with magnetic resonance imaging. J. Cogn. Neurosci. 4, 352–374 (1992)

    Article  Google Scholar 

  22. P.Tan, M. Steinbach and V. Kumar, Introduction to Data Mining. Pearson Addison Wesley (2006)

  23. Kanwisher, N., McDermott, J., Chun, M.: The fusiform face area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17(11), 4302–4311 (1997)

    Google Scholar 

  24. Gauthier, I., Tarr, M.J., Anderson, A.W., Skudlarski, P., Gore, J.C.: Activation of the middle fusiform ’face area’ increases with expertise in recognizing novel objects. Nat. Neurosci. 2, 568–573 (1999)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MEST)(2013-056480). This research was supported by the MSIP (Ministry of Science, ICT&Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-3005) supervised by the NIPA(National IT Industry Promotion Agency).

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  1. Department of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea

    Luu-Ngoc Do, Hyung-Jeong Yang, Soo-Hyung Kim, Guee-Sang Lee & Sun-Hee Kim

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  1. Luu-Ngoc Do
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  2. Hyung-Jeong Yang
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  3. Soo-Hyung Kim
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Correspondence to Hyung-Jeong Yang.

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Do, LN., Yang, HJ., Kim, SH. et al. A multi-voxel-activity-based feature selection method for human cognitive states classification by functional magnetic resonance imaging data. Cluster Comput 18, 199–208 (2015). https://doi.org/10.1007/s10586-014-0369-9

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  • DOI: https://doi.org/10.1007/s10586-014-0369-9

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